Classification of Covid-19 Variants Using Boosting Algorithm

COVID-19 is a disease caused by a virus from the coronavirus group, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The Sars-CoV-2 virus has 5 variants that are included in the variant of concern (VOC) namely Alpha, Beta, Delta, Gamma, and Omicron. The COVID-19 virus has infected more than 400 million people worldwide. This information causes a significant increase in data with the result that computations are needed to obtain knowledge (pattern) from the data. Machine learning is a tool that can facilitate the analysis of big data, one of which is classification. In this paper, we implement two boosting algorithms: eXtreme Gradient Boosting (XGB) and Light Gradient Boosting Machine (LGBM), to classify the Deoxyribonucleic acid (DNA) sequence data from the COVID-19 virus variants. Additionally, we utilized one-hot encoded method to encode data. The experiment results showed that XGB has better accuracy than LGBM, but LGBM has faster computation time than XGB. The highest accuracy is 0.992.